Hybrid dual-objective parallel genetic algorithm for heterogeneous multiprocessor scheduling.

Scheduling is a process of mapping resources to tasks and it's objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time. [ABSTRACT FROM AUTHOR]